SubjectsSubjects(version: 928)
Course, academic year 2021/2022
Laboratory and industrial data - M111006
Title: Laboratorní a průmyslová data
Guaranteed by: Department of Organic Technology (111)
Faculty: Faculty of Chemical Technology
Actual: from 2020
Semester: winter
Points: winter s.:4
E-Credits: winter s.:4
Examination process: winter s.:
Hours per week, examination: winter s.:1/2, C+Ex [HT]
Capacity: unlimited / unlimited (unknown)
Min. number of students: unlimited
Language: Czech
Teaching methods: full-time
For type: Master's (post-Bachelor)
Note: course can be enrolled in outside the study plan
enabled for web enrollment
Guarantor: Paterová Iva Ing. Ph.D.
Interchangeability : N111018
This subject contains the following additional online materials
Annotation -
Last update: Kubová Petra Ing. (04.01.2018)
The course focuses on informal, consistent and reliable processing of laboratory and industrial data. Emphasis is placed on the gaining experience of solving a large and varied set of practical examples from the field of chemical technology. The course also introduces the basics of design of experiments.
Aim of the course -
Last update: Kubová Petra Ing. (04.01.2018)

Students will be able to:

process data from laboratory experiments and industrial measurements,

comprehensively and reliably interpret the results of statistical data processing,

propose optimal design of experiments,

operate representative software for statistical data processing.

Literature -
Last update: Kubová Petra Ing. (04.01.2018)

A: Joglekar A. M.: Industrial statistics. Wiley, Hoboken 2010. 9780470497166

Learning resources -
Last update: Kubová Petra Ing. (04.01.2018)

Requirements to the exam - Czech
Last update: Kubová Petra Ing. (04.01.2018)

1. Podmínkou udělení zápočtu je zvládnutí základních počítačových programů pro zpracování a vyhodnocování dat, prověřené na základě dvou testů během semestru.

2. Zkouška je založena na samostatném zpracování vybraného souboru praktických příkladů s možností využití libovolných pomůcek.

Syllabus -
Last update: Kubová Petra Ing. (04.01.2018)

1. Principles of data analysis, properties of the measured data, experiments versus observations.

2. Statistical analysis of the data, sample problems, application software.

3. Direct acquisition of information from measured data, analysis of the sample characteristics.

4. Time series analysis, data sorting, design of experiments.

5. Mathematical models, mechanistic, empirical and semi-empirical models.

6. Methods of optimal estimation of model parameters, software for regression analysis.

7. Models with differential equations, derivatives of dependent variables, integration of differential equations.

8. Evaluating the reliability of regression parameters, confidence intervals, correlation of parameters.

9. Evaluating the reliability of simulated data, analysis of variance and residual variation.

10. Treatment of data for regression analysis, elimination of remote measurements, transformation of variables.

11. Treatment of regression models, model transformation, elimination of strong correlation of parameters.

12. Design of experiments, the optimum number of responses and the range of experimental conditions.

13. Sequential design of experiments, model discrimination and refinement.

14. Factorial and empirical design of experiments, full and fractional factorial design.

Registration requirements -
Last update: Kubová Petra Ing. (04.01.2018)

Mathematics I, Applied Statistics